Overview

Dataset statistics

Number of variables11
Number of observations557
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.8 KiB
Average record size in memory84.2 B

Variable types

Numeric11

Alerts

Average Temperature is highly correlated with Maximum temperature and 5 other fieldsHigh correlation
Maximum temperature is highly correlated with Average Temperature and 5 other fieldsHigh correlation
Minimum temperature is highly correlated with Average Temperature and 4 other fieldsHigh correlation
Atmospheric pressure at sea level (hPa) is highly correlated with Average Temperature and 3 other fieldsHigh correlation
Average relative humidity is highly correlated with Average Temperature and 1 other fieldsHigh correlation
Average visibility (Km) is highly correlated with Average Temperature and 3 other fieldsHigh correlation
Average wind speed (Km/h) is highly correlated with Maximum sustained wind speed (Km/h)High correlation
Maximum sustained wind speed (Km/h) is highly correlated with Average wind speed (Km/h)High correlation
PM2.5 is highly correlated with Average Temperature and 4 other fieldsHigh correlation
Average Temperature is highly correlated with Maximum temperature and 5 other fieldsHigh correlation
Maximum temperature is highly correlated with Average Temperature and 5 other fieldsHigh correlation
Minimum temperature is highly correlated with Average Temperature and 4 other fieldsHigh correlation
Atmospheric pressure at sea level (hPa) is highly correlated with Average Temperature and 3 other fieldsHigh correlation
Average relative humidity is highly correlated with Average Temperature and 1 other fieldsHigh correlation
Average visibility (Km) is highly correlated with Average Temperature and 3 other fieldsHigh correlation
Average wind speed (Km/h) is highly correlated with Maximum sustained wind speed (Km/h)High correlation
Maximum sustained wind speed (Km/h) is highly correlated with Average wind speed (Km/h)High correlation
PM2.5 is highly correlated with Average Temperature and 4 other fieldsHigh correlation
Average Temperature is highly correlated with Maximum temperature and 2 other fieldsHigh correlation
Maximum temperature is highly correlated with Average Temperature and 2 other fieldsHigh correlation
Minimum temperature is highly correlated with Average Temperature and 2 other fieldsHigh correlation
Atmospheric pressure at sea level (hPa) is highly correlated with Average Temperature and 2 other fieldsHigh correlation
Average wind speed (Km/h) is highly correlated with Maximum sustained wind speed (Km/h)High correlation
Maximum sustained wind speed (Km/h) is highly correlated with Average wind speed (Km/h)High correlation
df_index is highly correlated with Average Temperature and 6 other fieldsHigh correlation
Average Temperature is highly correlated with df_index and 6 other fieldsHigh correlation
Maximum temperature is highly correlated with df_index and 6 other fieldsHigh correlation
Minimum temperature is highly correlated with df_index and 5 other fieldsHigh correlation
Atmospheric pressure at sea level (hPa) is highly correlated with df_index and 6 other fieldsHigh correlation
Average relative humidity is highly correlated with df_index and 4 other fieldsHigh correlation
Average visibility (Km) is highly correlated with df_index and 7 other fieldsHigh correlation
Average wind speed (Km/h) is highly correlated with Average visibility (Km) and 1 other fieldsHigh correlation
Maximum sustained wind speed (Km/h) is highly correlated with Average wind speed (Km/h)High correlation
PM2.5 is highly correlated with df_index and 5 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
Total rainfall and / or snowmelt (mm) has 453 (81.3%) zeros Zeros
Average wind speed (Km/h) has 6 (1.1%) zeros Zeros

Reproduction

Analysis started2022-07-15 10:07:13.839100
Analysis finished2022-07-15 10:07:26.646667
Duration12.81 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct557
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean541.6786355
Minimum0
Maximum1090
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2022-07-15T15:37:26.720381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.2
Q1269
median541
Q3813
95-th percentile1032.2
Maximum1090
Range1090
Interquartile range (IQR)544

Descriptive statistics

Standard deviation314.6618817
Coefficient of variation (CV)0.5809014073
Kurtosis-1.198214936
Mean541.6786355
Median Absolute Deviation (MAD)272
Skewness0.00292360445
Sum301715
Variance99012.09978
MonotonicityStrictly increasing
2022-07-15T15:37:26.809931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.2%
7301
 
0.2%
7181
 
0.2%
7191
 
0.2%
7231
 
0.2%
7241
 
0.2%
7251
 
0.2%
7291
 
0.2%
7311
 
0.2%
7481
 
0.2%
Other values (547)547
98.2%
ValueCountFrequency (%)
01
0.2%
11
0.2%
21
0.2%
61
0.2%
71
0.2%
81
0.2%
121
0.2%
131
0.2%
141
0.2%
181
0.2%
ValueCountFrequency (%)
10901
0.2%
10861
0.2%
10851
0.2%
10841
0.2%
10801
0.2%
10791
0.2%
10781
0.2%
10741
0.2%
10731
0.2%
10721
0.2%

Average Temperature
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct222
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.06750449
Minimum6.7
Maximum38.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2022-07-15T15:37:26.922614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.7
5-th percentile12.38
Q118.4
median27.5
Q331
95-th percentile34.72
Maximum38.5
Range31.8
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation7.33027155
Coefficient of variation (CV)0.2924212721
Kurtosis-0.9730690226
Mean25.06750449
Median Absolute Deviation (MAD)4.7
Skewness-0.4451735902
Sum13962.6
Variance53.73288099
MonotonicityNot monotonic
2022-07-15T15:37:27.025955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.910
 
1.8%
31.98
 
1.4%
31.28
 
1.4%
30.47
 
1.3%
30.17
 
1.3%
31.87
 
1.3%
34.17
 
1.3%
17.26
 
1.1%
29.16
 
1.1%
31.66
 
1.1%
Other values (212)485
87.1%
ValueCountFrequency (%)
6.71
0.2%
7.41
0.2%
7.81
0.2%
8.61
0.2%
9.11
0.2%
9.31
0.2%
9.51
0.2%
9.81
0.2%
10.11
0.2%
10.41
0.2%
ValueCountFrequency (%)
38.51
 
0.2%
37.92
0.4%
37.82
0.4%
37.71
 
0.2%
37.61
 
0.2%
36.91
 
0.2%
36.81
 
0.2%
36.31
 
0.2%
36.23
0.5%
35.92
0.4%

Maximum temperature
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct218
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.96238779
Minimum9.8
Maximum45.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2022-07-15T15:37:27.126050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.8
5-th percentile18.98
Q127.3
median33.95
Q336.7
95-th percentile41.7
Maximum45.5
Range35.7
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation7.020364074
Coefficient of variation (CV)0.2196445435
Kurtosis-0.4016355655
Mean31.96238779
Median Absolute Deviation (MAD)4.65
Skewness-0.5426714012
Sum17803.05
Variance49.28551173
MonotonicityNot monotonic
2022-07-15T15:37:27.226327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.69
 
1.6%
35.89
 
1.6%
35.69
 
1.6%
40.68
 
1.4%
367
 
1.3%
35.57
 
1.3%
34.47
 
1.3%
34.97
 
1.3%
36.57
 
1.3%
34.56
 
1.1%
Other values (208)481
86.4%
ValueCountFrequency (%)
9.81
0.2%
12.71
0.2%
13.41
0.2%
14.51
0.2%
15.31
0.2%
15.51
0.2%
15.91
0.2%
161
0.2%
16.31
0.2%
16.81
0.2%
ValueCountFrequency (%)
45.53
0.5%
45.12
0.4%
452
0.4%
44.91
 
0.2%
44.62
0.4%
43.61
 
0.2%
43.21
 
0.2%
431
 
0.2%
42.91
 
0.2%
42.82
0.4%

Minimum temperature
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct210
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.03743268
Minimum0
Maximum32.7
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2022-07-15T15:37:27.310961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.48
Q111.9
median20.75
Q325.6
95-th percentile28.24
Maximum32.7
Range32.7
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation7.49778049
Coefficient of variation (CV)0.3938440975
Kurtosis-1.184062137
Mean19.03743268
Median Absolute Deviation (MAD)5.85
Skewness-0.3834288581
Sum10603.85
Variance56.21671228
MonotonicityNot monotonic
2022-07-15T15:37:27.411245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2713
 
2.3%
24.811
 
2.0%
2510
 
1.8%
289
 
1.6%
27.69
 
1.6%
25.38
 
1.4%
267
 
1.3%
25.87
 
1.3%
26.87
 
1.3%
25.47
 
1.3%
Other values (200)469
84.2%
ValueCountFrequency (%)
01
 
0.2%
2.41
 
0.2%
3.31
 
0.2%
42
0.4%
4.43
0.5%
4.82
0.4%
51
 
0.2%
5.21
 
0.2%
5.52
0.4%
5.64
0.7%
ValueCountFrequency (%)
32.71
0.2%
311
0.2%
30.81
0.2%
30.51
0.2%
30.22
0.4%
30.11
0.2%
301
0.2%
29.61
0.2%
29.31
0.2%
29.22
0.4%

Atmospheric pressure at sea level (hPa)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct241
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1008.335189
Minimum991.5
Maximum1023.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2022-07-15T15:37:27.511329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum991.5
5-th percentile996.78
Q11001.6
median1009
Q31015
95-th percentile1019.6
Maximum1023.2
Range31.7
Interquartile range (IQR)13.4

Descriptive statistics

Standard deviation7.567902486
Coefficient of variation (CV)0.007505344028
Kurtosis-1.220617113
Mean1008.335189
Median Absolute Deviation (MAD)6.7
Skewness-0.0970727311
Sum561642.7
Variance57.27314803
MonotonicityNot monotonic
2022-07-15T15:37:27.605084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000.88
 
1.4%
1012.87
 
1.3%
1014.87
 
1.3%
1003.46
 
1.1%
1001.86
 
1.1%
10025
 
0.9%
998.45
 
0.9%
998.85
 
0.9%
1017.15
 
0.9%
1005.85
 
0.9%
Other values (231)498
89.4%
ValueCountFrequency (%)
991.51
0.2%
993.11
0.2%
993.21
0.2%
993.31
0.2%
9942
0.4%
994.11
0.2%
994.71
0.2%
994.91
0.2%
9951
0.2%
995.11
0.2%
ValueCountFrequency (%)
1023.21
 
0.2%
1022.31
 
0.2%
1021.91
 
0.2%
1021.23
0.5%
1021.11
 
0.2%
10213
0.5%
1020.72
0.4%
1020.63
0.5%
1020.51
 
0.2%
1020.21
 
0.2%

Average relative humidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.40035907
Minimum21
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2022-07-15T15:37:27.689748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile36
Q156
median67
Q376
95-th percentile88.2
Maximum98
Range77
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.09381454
Coefficient of variation (CV)0.2307910041
Kurtosis-0.07282520467
Mean65.40035907
Median Absolute Deviation (MAD)9
Skewness-0.4340593205
Sum36428
Variance227.8232373
MonotonicityNot monotonic
2022-07-15T15:37:27.805629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7121
 
3.8%
6820
 
3.6%
7620
 
3.6%
5417
 
3.1%
6917
 
3.1%
6516
 
2.9%
6116
 
2.9%
7316
 
2.9%
5915
 
2.7%
7415
 
2.7%
Other values (63)384
68.9%
ValueCountFrequency (%)
211
 
0.2%
241
 
0.2%
262
 
0.4%
295
0.9%
304
0.7%
311
 
0.2%
325
0.9%
332
 
0.4%
342
 
0.4%
354
0.7%
ValueCountFrequency (%)
981
 
0.2%
972
 
0.4%
961
 
0.2%
952
 
0.4%
942
 
0.4%
934
0.7%
924
0.7%
914
0.7%
906
1.1%
892
 
0.4%

Total rainfall and / or snowmelt (mm)
Real number (ℝ≥0)

ZEROS

Distinct32
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.82486535
Minimum0
Maximum122.94
Zeros453
Zeros (%)81.3%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2022-07-15T15:37:27.890302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9.094
Maximum122.94
Range122.94
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.332518136
Coefficient of variation (CV)4.566100252
Kurtosis112.6596625
Mean1.82486535
Median Absolute Deviation (MAD)0
Skewness9.375625261
Sum1016.45
Variance69.43085848
MonotonicityNot monotonic
2022-07-15T15:37:27.974979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0453
81.3%
2.0316
 
2.9%
0.5111
 
2.0%
4.068
 
1.4%
0.258
 
1.4%
6.16
 
1.1%
7.116
 
1.1%
1.025
 
0.9%
3.054
 
0.7%
12.954
 
0.7%
Other values (22)36
 
6.5%
ValueCountFrequency (%)
0453
81.3%
0.258
 
1.4%
0.5111
 
2.0%
0.763
 
0.5%
1.025
 
0.9%
2.0316
 
2.9%
3.054
 
0.7%
4.068
 
1.4%
5.083
 
0.5%
6.16
 
1.1%
ValueCountFrequency (%)
122.941
 
0.2%
951
 
0.2%
56.91
 
0.2%
38.11
 
0.2%
36.071
 
0.2%
321
 
0.2%
29.971
 
0.2%
26.921
 
0.2%
25.913
0.5%
23.111
 
0.2%

Average visibility (Km)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.783123878
Minimum0.3
Maximum3.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2022-07-15T15:37:28.043772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.6
Q11.4
median1.9
Q32.1
95-th percentile2.7
Maximum3.4
Range3.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.6034990366
Coefficient of variation (CV)0.3384504263
Kurtosis-0.1572474565
Mean1.783123878
Median Absolute Deviation (MAD)0.4
Skewness-0.3065519935
Sum993.2
Variance0.3642110871
MonotonicityNot monotonic
2022-07-15T15:37:28.112787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1.9198
35.5%
2.652
 
9.3%
2.332
 
5.7%
1.832
 
5.7%
1.632
 
5.7%
1.429
 
5.2%
0.823
 
4.1%
1.323
 
4.1%
1.122
 
3.9%
0.620
 
3.6%
Other values (10)94
16.9%
ValueCountFrequency (%)
0.34
 
0.7%
0.513
2.3%
0.620
3.6%
0.823
4.1%
120
3.6%
1.122
3.9%
1.323
4.1%
1.429
5.2%
1.632
5.7%
1.832
5.7%
ValueCountFrequency (%)
3.41
 
0.2%
3.21
 
0.2%
3.18
 
1.4%
2.93
 
0.5%
2.717
 
3.1%
2.652
 
9.3%
2.413
 
2.3%
2.332
 
5.7%
2.114
 
2.5%
1.9198
35.5%

Average wind speed (Km/h)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct93
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.445780969
Minimum0
Maximum24.4
Zeros6
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2022-07-15T15:37:28.224387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9
Q13.3
median5.9
Q38.9
95-th percentile14.6
Maximum24.4
Range24.4
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.107197268
Coefficient of variation (CV)0.6371915657
Kurtosis0.6135296869
Mean6.445780969
Median Absolute Deviation (MAD)2.8
Skewness0.7965000635
Sum3590.3
Variance16.8690694
MonotonicityNot monotonic
2022-07-15T15:37:28.335685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.716
 
2.9%
7.215
 
2.7%
8.115
 
2.7%
0.914
 
2.5%
2.614
 
2.5%
5.414
 
2.5%
4.814
 
2.5%
4.413
 
2.3%
6.313
 
2.3%
0.413
 
2.3%
Other values (83)416
74.7%
ValueCountFrequency (%)
06
1.1%
0.413
2.3%
0.73
 
0.5%
0.914
2.5%
1.18
1.4%
1.38
1.4%
1.58
1.4%
1.75
 
0.9%
1.913
2.3%
211
2.0%
ValueCountFrequency (%)
24.41
0.2%
20.71
0.2%
18.71
0.2%
18.51
0.2%
17.81
0.2%
17.61
0.2%
17.41
0.2%
17.21
0.2%
171
0.2%
16.92
0.4%

Maximum sustained wind speed (Km/h)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.59210054
Minimum1.9
Maximum57.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2022-07-15T15:37:28.428525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile5.4
Q111.1
median14.8
Q318.3
95-th percentile27.8
Maximum57.6
Range55.7
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation7.921367747
Coefficient of variation (CV)0.5080372415
Kurtosis6.364189663
Mean15.59210054
Median Absolute Deviation (MAD)3.7
Skewness1.794754392
Sum8684.8
Variance62.74806698
MonotonicityNot monotonic
2022-07-15T15:37:28.492544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
14.8104
18.7%
11.181
14.5%
18.360
10.8%
7.653
9.5%
9.442
7.5%
22.240
 
7.2%
1330
 
5.4%
16.523
 
4.1%
20.621
 
3.8%
3.521
 
3.8%
Other values (15)82
14.7%
ValueCountFrequency (%)
1.91
 
0.2%
3.521
 
3.8%
5.411
 
2.0%
7.653
9.5%
9.442
7.5%
11.181
14.5%
1330
 
5.4%
14.8104
18.7%
16.523
 
4.1%
18.360
10.8%
ValueCountFrequency (%)
57.61
 
0.2%
55.44
 
0.7%
51.91
 
0.2%
501
 
0.2%
48.21
 
0.2%
40.71
 
0.2%
35.21
 
0.2%
33.53
 
0.5%
31.71
 
0.2%
29.410
1.8%

PM2.5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct226
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.5870736
Minimum2
Maximum422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2022-07-15T15:37:28.591841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile26
Q149
median91
Q3158
95-th percentile278
Maximum422
Range420
Interquartile range (IQR)109

Descriptive statistics

Standard deviation82.21295198
Coefficient of variation (CV)0.7174714336
Kurtosis0.3800254068
Mean114.5870736
Median Absolute Deviation (MAD)48
Skewness1.05038466
Sum63825
Variance6758.969473
MonotonicityNot monotonic
2022-07-15T15:37:28.696295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9117
 
3.1%
388
 
1.4%
478
 
1.4%
807
 
1.3%
437
 
1.3%
306
 
1.1%
316
 
1.1%
256
 
1.1%
416
 
1.1%
1106
 
1.1%
Other values (216)480
86.2%
ValueCountFrequency (%)
22
0.4%
71
0.2%
121
0.2%
131
0.2%
141
0.2%
151
0.2%
161
0.2%
192
0.4%
202
0.4%
212
0.4%
ValueCountFrequency (%)
4221
0.2%
3761
0.2%
3652
0.4%
3531
0.2%
3491
0.2%
3371
0.2%
3341
0.2%
3321
0.2%
3231
0.2%
3221
0.2%

Interactions

2022-07-15T15:37:25.312027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:15.113871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:16.191912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:17.224950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:18.324482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:19.424428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:20.402050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:21.391905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:22.461462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:23.437140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:24.368388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:25.393669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:15.198681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:16.276685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:17.317725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:18.435462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:19.513216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:20.490073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:21.478031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:22.564570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:23.522704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-07-15T15:37:15.278546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:16.370403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:17.414524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:18.533288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:19.605093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:20.578847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:21.560303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:22.654818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:23.607076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:24.539522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:25.571446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-07-15T15:37:16.553945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:17.608746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:18.798840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-07-15T15:37:15.834836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-07-15T15:37:24.123456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:25.054899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:26.198764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:16.017380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:17.053765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:18.120029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:19.253345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:20.234459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:21.218080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:22.308622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:23.249897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:24.193729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:25.144034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:26.281954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:16.110117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:17.140532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:18.223752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:19.327521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:20.321532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:21.318161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:22.393578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:23.353581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:24.291272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-15T15:37:25.228597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-07-15T15:37:28.790046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-15T15:37:28.927905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-15T15:37:29.069064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-15T15:37:29.222422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-15T15:37:26.423871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-15T15:37:26.577856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexAverage TemperatureMaximum temperatureMinimum temperatureAtmospheric pressure at sea level (hPa)Average relative humidityTotal rainfall and / or snowmelt (mm)Average visibility (Km)Average wind speed (Km/h)Maximum sustained wind speed (Km/h)PM2.5
009.115.34.01015.690.00.00.50.014.8284
117.49.84.81017.693.00.00.54.39.4239
227.812.74.41018.587.00.00.64.411.1182
366.713.42.41019.482.00.00.64.811.1264
478.615.53.31018.772.00.00.88.120.6223
5812.420.94.41017.361.00.01.38.722.2200
61216.025.210.01013.279.00.00.64.811.1285
71313.421.09.21015.187.00.00.51.57.6334
81414.322.66.61016.376.00.00.80.43.5276
91812.718.97.31021.276.00.01.86.116.5108

Last rows

df_indexAverage TemperatureMaximum temperatureMinimum temperatureAtmospheric pressure at sea level (hPa)Average relative humidityTotal rainfall and / or snowmelt (mm)Average visibility (Km)Average wind speed (Km/h)Maximum sustained wind speed (Km/h)PM2.5
547107214.821.006.801016.463.00.02.45.413.0163
548107314.621.306.001017.159.00.02.68.114.8161
549107415.222.207.001016.560.00.02.47.014.8157
550107814.421.106.801019.165.00.01.65.613.0245
551107912.621.000.001018.968.00.01.42.69.4264
552108013.721.005.601018.871.00.01.31.57.6253
553108413.319.305.501021.962.00.01.97.814.8181
554108514.421.505.501021.060.00.01.88.918.3210
555108616.322.507.601020.664.00.01.48.114.8255
556109027.533.9520.751009.067.00.01.95.914.8220